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ThesisItem Open Access Design analysis of suitable cutter header assembly for pokkali paddy harvester(Department of Farm Machinery and power engineering, KCAET, Tavanur, 2021) Rathinavel, S; KAU; Sindhu, BhaskarPokkali system of rice cultivation is a unique farming under pokkali ecosystem prevalent exclusively in central Kerala. Harvesting of paddy in pokkali is to be carried out under stagnated water level which may raise upto five feet. Hence a research was undertaken to design a suitable cutter header assembly for the harvester, as other existing harvesting machines can’t be used in pokkali fields. Existing machines were studied and their drawbacks were identified. Four different designs of cutter header assembly models were made suiting an existing amphibian weed harvester (Truxor DM 5045). The four design models were design I (multiple bat reel system), design II (single bat reel system), design III (vertical axis reel system) and design IV (floating assembly with projected conveyor system). Selection of the best model out of these four was carried out by Quality Function Deployment (QFD) Technique along with Analytical Hierarchy Process (AHP) as a sub process, statistical analysis with expert ratings on models and Finite Element Analysis (FEM) on components suspected to failure of the selected models. All the results were collectively analysed and design II (single bat reel system) was selected. Also the design I (multiple bat reel system) can be an alternative as per statistical analysis. The single bat reel type system consists of single bat reel with elongated tynes. The corrosion resistant standard type cutterbar is finalized. Other specifications such as position of assembly, material of construction, dimensions etc were discussed in detail. Further the design, development and evaluation are suggested on the selected models with reduced size machine with same features and components as in Truxor DM 5045 (Amphibian weed harvesting machine)ThesisItem Open Access Development of a machine vision system to identify matured pepper spikes(Department of Farm Machinery and Power Engineering, Kelappaji College of Agricultural Engineering and Technology, Tavanur, 2021) Meera, T; KAU; Sindhu, BhaskarBlack pepper is a perennial crop and one of the most economically significant spices in India. It has a high commercial value in the market all around the world. Its fruit is harvested, dried and powdered for many cuisines and processed for many value added products. Black pepper is a flowering vine growing up to 4 m in height. The berries turns from green to red on maturity and are harvested when it starts to turn red. For achieving good quality and good sized pepper, it should be harvested at its proper matured state. Farmers for their time saving and due to heavy work intensity, harvest almost all the fruits which are in a range of maturity along with the real matured ones. This eventually affects the crop yield and quality. Hence employing an automated identification system in this case would be effective. An application programme interface was developed for this, using the fruit features like the shape, colour and size. By using the machine learning techniques and computer vision technology, two programmes were developed in python language, one using OpenCV library and Haar Cascade classifier, and other platform with TensorFlow as library and faster-RCNN as classifier. Studies were also carried out to analyse the physical properties of black pepper. Using image acquisition, a dataset was created and was used for training and preparation of both the models. The hardware part of the system comprised of a webcam as sensor, Raspberry Pi processor, a RPI display unit and some accessory parts. The hardware and software parts were installed and assembled, and subjected to performance evaluation. It was revealed that the Tf-RCNN platform had better performance and efficiency. The performance evaluation parameters viz., sensitivity, specificity and accuracy values were 78%, 71% and 75% respectively for the second model. It was statistically verified that there is a significant difference between the two platforms and the second model had better consistency.